Learning Perception and Control for Robot Intelligence

PhD Thesis Proposal Defence
Title: "Learning Perception and Control for Robot Intelligence"
by
Mr. Siyi LI
Abstract:
Autonomous robots that can assist humans in the daily unstructured world have
been a long standing vision of robotics and artificial intelligence. The past
few years have seen major advances in many perception and control tasks using
deep learning and reinforcement learning methods. In this thesis proposal, we
propose to explore learning-based solutions to robotic tasks.
Our first attempt is building a unified benchmark for visual object tracking on
the unmanned aerial vehicle (UAV) platform. We manually build a drone tracking
dataset, consisting of a variety of videos with high diversity captured by
drone cameras. We devise new motion models for the drone tracking scenario by
explicitly estimating the camera motion in the tracking phase.
Collecting real-world data with robotic systems is generally expensive due to
the hardware cost and the manual labeling effort. However, deep learning and
reinforcement learning methods require a data-hungry training paradigm. We
propose to address this issue by learning in simulation and achieve successful
transfer to real world. For robotic perception task, we investigate instance
segmentation for robot manipulation. We develop an automated rendering pipeline
to generate photorealistic synthetic images with pixel-level labels. The
synthetic dataset is then used to train an objectness deep neural network model
which can successfully generalize to real-world manipulation scenarios. For
robotic control task, we focus on the problem of learning UAV control for
actively tracking a moving target. We propose a hierarchical approach that
combines model-free reinforcement learning methods with conventional feedback
controllers to enable efficient and safe exploration in the training phase. We
show that this hierarchical control scheme can learn a target following policy
in a simulator efficiently and the learned behavior can be successfully
transferred to real-world quadrotor control.
Date: Wednesday, 12 September 2018
Time: 3:30pm - 5:30pm
Venue: Room 4475
(lifts 25/26)
Committee Members: Prof. Dit-Yan Yeung (Supervisor)
Dr. Yangqiu Song (Chairperson)
Dr. Raymond Wong
Prof. Nevin Zhang
**** ALL are Welcome ****